class_docstr: Apply a random transformation to the brightness, contrast, saturation and hue of a tensor image.
  
  This implementation aligns PIL. Hence, the output is close to TorchVision. However, it does not
  follow the color theory and is not be actively maintained. Prefer using
  :func:`kornia.augmentation.ColorJiggle`
  
  .. image:: _static/img/ColorJitter.png
  
  Args:
      p: probability of applying the transformation.
      brightness: The brightness factor to apply.
      contrast: The contrast factor to apply.
      saturation: The saturation factor to apply.
      hue: The hue factor to apply.
      silence_instantiation_warning: if True, silence the warning at instantiation.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.adjust_brightness_accumulative`,
      :func:`kornia.enhance.adjust_contrast_with_mean_subtraction`,
      :func:`kornia.enhance.adjust_saturation_with_gray_subtraction`,
      :func:`kornia.enhance.adjust_hue`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.ones(1, 3, 3, 3)
      >>> aug = ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.)
      >>> aug(inputs)
      tensor([[[[0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993]],
      <BLANKLINE>
               [[0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993]],
      <BLANKLINE>
               [[0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
  
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      brightness: Union[
          Tensor, float, Tuple[float, float], List[float]
      ] = 0.0,
      contrast: Union[
          Tensor, float, Tuple[float, float], List[float]
      ] = 0.0,
      saturation: Union[
          Tensor, float, Tuple[float, float], List[float]
      ] = 0.0,
      hue: Union[Tensor, float, Tuple[float, float], List[float]] = 0.0,
      same_on_batch: bool = False,
      p: float = 1.0,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, keepdim=keepdim
      )
  
      self.brightness = brightness
      self.contrast = contrast
      self.saturation = saturation
      self.hue = hue
      self._param_generator = rg.ColorJitterGenerator(
          brightness, contrast, saturation, hue
      )
  
file_path: kornia/kornia/augmentation/_2d/intensity/color_jitter.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.constants import pi
  from kornia.core import Tensor
  from kornia.enhance import adjust_brightness_accumulative, adjust_contrast_with_mean_subtraction, adjust_hue, adjust_saturation_with_gray_subtraction
label_desc: Use image node_id 1 to create a new ColorJitter object from inherited base classes: IntensityAugmentationBase2D with example: obj = ColorJitter(brightness, contrast, saturation, hue, same_on_batch, p, keepdim)